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. 2025 Oct 5;11(5):504–516. doi: 10.33546/bnj.3977

Prevalence and related factors of healthy aging: A systematic review and meta-analysis

Linxi Tang 1, Nur Syahmina Binti Rasudin 1, Yuan Dong 2,*, Azlina Yusuf 1,*
PMCID: PMC12498238  PMID: 41059004

Abstract

Background

Healthy aging is a key goal of global public health and aging policy initiatives. Understanding its prevalence and associated determinants is essential for designing targeted interventions and promoting well-being among older adults.

Objective

This study aimed to estimate the global prevalence of healthy aging and to identify its associated factors across different countries.

Methods

A systematic review and meta-analysis were conducted. A comprehensive literature search was conducted in Web of Science, PubMed, EBSCO Discovery Service (EDS), Scopus, and ProQuest from database inception to February 2025. Two independent reviewers screened articles, extracted data, and assessed study quality using the Newcastle-Ottawa Quality Assessment Scale (NOS) and the Agency for Healthcare Research and Quality (AHRQ) Methodology Checklist. Eligible studies were included in a meta-analysis using Stata 18.0 and R version 4.5.0.

Results

A total of 39 studies involving 300,624 participants were included. The pooled prevalence of healthy aging was 23.0% (95% CI: 18%–27%). After adjusting for publication bias using the trim-and-fill method, the estimate decreased to 15.6% (95% CI: 11.0%–20.1%), suggesting possible overestimation in the original estimate. Significant associations with healthy aging were found for age (≥75 years), gender, marital status, educational level, economic level, social participation, employment status, economy, smoking status, alcohol consumption, physical activity, body mass index (BMI), and self-rated health.

Conclusions

The findings indicate that fewer than one in four older adults meet the criteria for healthy aging globally, with substantial variation across regions. A wide range of sociodemographic, behavioral, and health-related factors influence this outcome. These results underscore the importance of addressing modifiable determinants in future public health efforts to promote healthy aging.

Registry

PROSPERO [CRD42024542942]

Keywords: healthy aging, prevalence, associated factors, systematic review, meta-analysis, older adults

Background

The global trend of population aging is accelerating at an unprecedented rate, presenting significant implications for public health and economic systems worldwide. Structural transformations, characterized by sustained declines in birth rates and increasing life expectancy, are propelling the transition to an era characterized by low population growth and elevated aging rates. Projections indicate that by 2040, global life expectancy is expected to reach 77.8 years for men and 82.5 years for women (Foreman et al., 2018). This rise in life expectancy correlates with an expanding proportion of older adults, leading to a pronounced increase in healthcare demands, particularly in the management of chronic diseases and the provision of long-term care services. Such demographic shifts pose substantial challenges to societal frameworks, emphasizing the urgent need for effective public policies to address the multifaceted impacts of aging on labor markets, healthcare services, and social security systems.

As the elderly population continues to expand, the anticipated decline in physical function among older adults is expected to significantly escalate the demand for healthcare and long-term care services (Martinez-Lacoba et al., 2021). This burgeoning demand presents considerable challenges to existing social security systems, complicating the efficient allocation of resources. Particularly in developing countries, many older adults encounter persistent and systemic health inequalities that restrict their access to essential healthcare services. According to projections by the World Health Organization (WHO), by 2050, over 80% of the elderly population is anticipated to reside in low- and middle-income countries (Leung et al., 2022). This demographic transition will likely exacerbate existing health challenges, especially in regions with underdeveloped healthcare infrastructure and limited resources. Importantly, increased longevity has not necessarily translated into improved health status for most older adults (Beard et al., 2016). Thus, addressing the specific needs of aging populations, particularly those in resource-constrained environments, has emerged as an urgent priority. Targeted, individualized, and comprehensive interventions are imperative for enhancing health outcomes and promoting healthy aging among older adults.

In response to these pressing health challenges, the United Nations has designated the period from 2021 to 2030 as the Decade of Healthy Aging, with the objective of fostering the health and well-being of older adults (WHO, 2021). The WHO defines Healthy Aging (HA) as the process of developing and maintaining functional ability that enables well-being in older age (WHO, 2015). This process encompasses not only physical health but also psychological well-being and social participation, thereby highlighting the multifaceted needs of older individuals in terms of their overall quality of life (Michel & Sadana, 2017).

In recent years, an increasing number of systematic reviews and meta-analyses have explored the prevalence and determinants of healthy aging. For example, Zhou et al. (2025) provided important evidence on global prevalence rates and key associated factors such as marital status, educational attainment, and place of residence. While these contributions have advanced the field, several limitations remain. First, previous reviews are often constrained by restricted time frames and have not incorporated the most recent data, particularly from emerging regions. Second, the scope of subgroup and stratified analyses has generally been limited, with insufficient exploration of important demographic and behavioral variables. Third, most existing studies focus on single regions or high-income countries, leaving cross-regional comparisons and evidence from low- and middle-income countries underrepresented.

To address these gaps, the present study systematically synthesized the latest global literature up to February 2025, with an emphasis on broadening geographical and temporal coverage, especially in Asia, Latin America, and Africa. We conducted comprehensive subgroup analyses across a broader range of sociodemographic and health-related factors, allowing for a more nuanced examination of variations in healthy aging across diverse populations. In addition, rigorous statistical procedures were implemented to ensure the robustness and reliability of the findings. By building upon and extending prior research, this study aims to provide a more comprehensive and up-to-date evidence base to inform future healthy aging policies and research.

Methods

Study Design

This systematic review and meta-analysis adhered to the guidelines specified by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The study protocol has been registered with PROSPERO (www.crd.york.ac.uk/prospero/), under registration number CRD42024542942.

Search Strategy

We conducted a comprehensive search using both Medical Subject Headings (MeSH) and free-text terms. Key terms included “Healthy Aging,” “Successful Aging,” “Active Aging,” and “Prevalence Rate.” We searched the databases Web of Science, PubMed, EBSCO Discovery Service (EDS), Scopus, and ProQuest from inception to February 2025. Details of the specific search strategy are provided in Supplementary File S3.

Study Selection

Two reviewers independently screened titles and abstracts to exclude studies unrelated to the research objectives. Subsequently, the full texts of relevant studies were independently reviewed by both authors to determine eligibility based on predefined inclusion criteria. Discrepancies were addressed through consultation with a third reviewer, and a final agreement was reached following in-depth discussions. Inclusion criteria were: (1) observational studies; (2) studies that reported both sample size and the prevalence of healthy aging; and (3) studies with full texts available for download. (4) articles are written in English. Exclusion criteria were: (1) studies published in the form of reviews, conference abstracts, or dissertations; (2) duplicate studies; and (3) studies with insufficient or non-extractable data.

Data Extraction

Two authors independently extracted data, including the first author’s name, country of the study, publication year, study design, healthy aging assessment tools, study location, mean age, proportion of female participants, sample size, number of healthy aging cases, as well as prevalence and odds ratios (OR) with 95% confidence intervals (CI) related to healthy aging and its influencing factors. Information on risk of bias assessment elements was also extracted.

Assessment of Quality

The assessment of study quality was conducted based on the design of the studies. For those categorized as cross-sectional, the evaluation was carried out using the Agency for Healthcare Research and Quality (AHRQ) checklist, consisting of 11 items scored as “Yes” (1 point) or “No/Unclear” (0 points). Scores of 0-3, 4-7, and 8-11 indicated low, medium, and high quality, respectively (Ma et al., 2020; Zeng et al., 2015). For cohort studies, the Newcastle-Ottawa Scale (NOS) was used, covering three domains with a maximum score of 9. Scores of 0-3, 4-6, and 7-9 indicated low, medium, and high quality, respectively (Stang, 2010). Two trained researchers independently assessed the quality of each included study, cross-checked results, and resolved any disagreements through discussion, with a third-party adjudication if consensus was not achieved.

Data Analysis

Statistical analyses were conducted using Stata version 18.0 and R version 4.5.0. The pooled prevalence of healthy aging and associated influencing factors were calculated using Stata, with results reported as pooled rates with 95% confidence intervals (CI). Odds ratios (OR) with 95% CI were calculated to assess the impact of each influencing factor. Study heterogeneity was evaluated using the I² statistic; a fixed-effects model was applied when I² < 50% and p > 0.10, indicating low heterogeneity, whereas a random-effects model was used when I² ≥ 50% and p ≤ 0.10, indicating significant heterogeneity (Higgins et al., 2003). In cases of substantial heterogeneity, subgroup analyses were performed based on study characteristics, and sensitivity analyses were conducted using the leave-one-out method. Publication bias was assessed using funnel plots, Egger’s test, and Begg’s test, performed in R (version 4.5.0) with the “meta” package (version 8.1-0). In addition, the trim-and-fill method was applied to adjust for potential publication bias. Statistical significance was set at α = 0.05.

Results

Results of Literature Retrieval

A thorough search of five databases identified a total of 8,720 studies. After removing duplicate entries (n = 5949), the titles and abstracts of 2771 studies were screened. Of these, 2698 studies did not meet the inclusion criteria and were excluded. The full texts of 73 articles were downloaded for further review, resulting in the exclusion of 34 additional studies. Ultimately, 39 articles were included in this systematic review, which collectively represent the current body of evidence addressing the research question (Figure 1).

Figure 1.

Figure 1

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PRISMA flow diagram for database search of studies

Characteristics and Quality Evaluation of the Included Studies

The 39 included studies encompassed 300,624 participants and were conducted across five continents: Europe (n = 11), South America (n = 2), Africa (n = 1), North America (n = 5), and Asia (n = 20). The included studies were published between 2012 and 2024. The basic characteristics of the included studies are detailed in Table 1. Among the 39 articles, 23 were classified as high-quality studies, while 16 were rated as moderate. A comprehensive quality assessment of these studies is provided in Supplementary File S4.

Table 1.

Characteristics of the included studies

No. Author (Year) Country/Region Continent Study Design Age Female Ratio (%) Sample Size Number of HAd Factors related to HAc
1 Pac et al. (2019) Poland Europe Cross sectional 79.4±8.72 51.8 4653 819 (3)(4)(5)(7)(9)(11)(13)(16)(19)
2 Bosnes et al. (2017) Norway Europe Cohort 75.3±3.50 54.4 4497 702 (1)(2)(4)(9)(10)(11)(14)(19)
3 Pengpid and Peltzer (2021a) South Africa Africa Cross sectional 59.00 56.8 3734 1367 (1)(2)(9)(12)(22)
4 Mikkola et al. (2023) Finland Europe Cohort 76.00±3.00 56.2 813 159 (9)(18)(23)
5 Atallah et al. (2018) France Europe Cohort 49.00±6.00 45.7 2203 868 (21)
6 Zhou et al. (2021) Singapore Asia Cohort 53.3±6.10 59.0 14159 2834 (9)(10)(11)(12)(21)
7 Yeverino-Castro et al. (2023) Mexico North America Cohort 67.00 58.1 9160 1080 (1)(2)(3)(4)(5)(10)(14)(17)(18)
8 Bosch-Farré et al. (2018) Fourteen countriesa Europe Cross sectional 65.24±0.18 53.2 52641 12370 (1)(2)(4)(5)(7)
9 Schietzel et al. (2022) Five countriesb Europe Cross sectional 74.9±4.40 41.8 2123 887 (1)(2)(12)
10 Nguyen et al. (2020) UK Europe Cohort 66.4±10.30 55.5 9171 5502 (18)
11 Hamer et al. (2014) UK Europe Cohort 63.7±8.9 19.3 3454 665 (1)(2)(3)(5)(9)(10)
12 Kim et al. (2021) Korea Asia Cohort 65.00 57.5 2960 139 (1)(2)(18)(24)
13 Lu et al. (2022) Japan Asia Cohort 73.1±5.10 51.1 7226 574 (6)
14 Rodriguez-Laso et al. (2018) Spain Europe Cross sectional 74.5 56.8 690 31 (1)(2)(3)(4)
15 Mclaughlin et al. (2020) China Asia Cohort 73.0 51.0 4614 388 (1)(2)(4)(5)(8)
16 Nurrika et al. (2020) Indonesia Asia Cohort ≥50 33.6 696 206 (3)(4)(5)(6)(9)(11)(12)(18)(20)
17 Yin et al. (2021) China Asia Cohort ≥65 54.5 4690 741 (1)(3)(4)(5)(8)(11)
18 Mclaughlin et al. (2012) USA North America Cohort ≥65 57.9 9996 317 (1)(2)(4)
19 Pengpid and Peltzer (2021b) India Asia Cohort ≥65 52.2 21343 5805 (1)(2)(3)(4)(5)(8)(11)(12)(25)
20 Subramaniam et al. (2019) Singapore Asia Cross sectional ≥60 56.5 2565 651 (1)(4)(5)(22)
21 Ghazali et al. (2023) Malaysia Asia Cross sectional 67.7±5.80 64.1 765 108 (5)(6)(21)(23)(26)
22 Kang et al. (2024) Korea Asia Cohort ≥65 54.3 7708 3039 (8)
23 Bosnes et al. (2017) Norway Europe Cross sectional 76.7±4.70 54.6 5773 839 (1)(2)(4)(9)(10)(11)(27)
24 Meng and D'arcy (2013) Canada North America Cross sectional ≥65 None 8154 2878 (1)(3)(10)(11)(13)(18)(27)
25 Canêdo et al. (2018) Brazil South America Cross sectional 76.60 71.4 845 211 (1)(2)(3)(4)(5)(7)(10)(11)(12)(17)(18)(28)
26 Arias-Merino et al. (2012) Mexico North America Cross sectional 72.41±8.47 62.5 3116 394 (1)(2)(3)(4)
27 Li et al. (2014) Taiwan, China Asia Cross sectional 73.90 47.2 903 94 (1)(18)(19)(29)
28 Nakagawa et al. (2021) China, Korea, Japan Asia Cohort ≥65 51.6 6479 1140 (1)(2)(4)(8)
29 Curcio et al. (2018) Colombia South America Cohort 69.1±2.80 51.7 311 72 (2)(3)(4)(5)(13)(18)(30)
30 Zhao, Yu, et al. (2023) China Asia Cohort ≥60 51.8 7689 3986 (1)(2)(3)(4)(7)(8)(9)(10)(12)(21)(31)
31 Anantanasuwong et al. (2022) Thailand Asia Cross sectional 67.0 52.3 5092 3055 (1)(5)(11)(26)
32 Shafiee et al. (2020) Iran Asia Cross sectional 66.9±23.70 53.0 975 236 (1)(2)(4)(5)(20)(21)
33 Chang et al. (2022) China Asia Cohort ≥60 52.1 3074 180 (1)(4)(5)(13)(18)(20)(27)
34 Dahany et al. (2014) France Europe Cross sectional 70.1±3.0 61.8 2160 645 (11)(12)(14)(18)(26)
35 Zhao, Tang, et al. (2023) China Asia Cohort ≥60 49.6 3352 131 (10)(19)
36 Shi et al. (2016) China Asia Cohort ≥65 56.1 2296 891 (1)(2)(3)(4)(7)(14)(18)(21)(32)
37 Kim (2023) Korea Asia Cross sectional ≥65 55.4 73942 18651 (1)(2)(3)(4)(5)(7)(9) (11)(12)(19)(27)(33)
38 García-Lara et al. (2017) Mexico North America Cross sectional 79.6±7.10 59.4 935 94 (2)(4)(5)(14)(18)(31)
39 Liu et al. (2017) China Asia Cohort ≥60 47.3 5667 746 (1)(2)(3)(4)(8)
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Note:

a.

Fourteen European countries were included: Austria, Belgium, Czech Republic, Denmark, Estonia, France, Germany, Italy, Luxembourg, the Netherlands, Slovenia, Spain, Sweden, and Switzerland.

b.

Five European countries were included: Austria, France, Germany, Portugal, and Switzerland.

c.

Numbers represent factors related to healthy aging: (1) Age; (2) Gender; (3) Marital Status; (4) Education Level; (5) Economic Status; (6) Social Participation; (7) Employment Status; (8) Residential Area; (9) Smoking; (10) Alcohol Consumption; (11) Physical Exercise; (12) Body Mass Index (BMI); (13) Self-rated Health; (14) Diabetes; (15) Hypertension; (16) Falls; (17)Visual Impairment; (18) Other Chronic Diseases; (19) Social Support; (20) Health Insurance; (21) Lifestyle; (22) Race/Ethnicity; (23) Nutrition; (24) Functional Capacity; (25) Childhood Health; (26) Religion; (27) Life Satisfaction; (28) Activities of Daily Living (ADLs); (29) Frailty; (30) Spirituality; (31) Living Conditions; (32) Sleep Quality; (33) Environmental Factors.

d.

Number of HA refers to the number of participants in each study who achieved healthy aging according to the criteria used by the original authors.

Meta-analysis Results of Healthy Aging Prevalence

Overall Prevalence of Healthy Aging

A meta-analysis of 39 studies was conducted to estimate the global prevalence of healthy aging. Substantial heterogeneity was observed across studies (I² = 99.92%), warranting the use of a random-effects model. The pooled estimate indicated a global prevalence of 23% (95% CI: 18%–27%), as presented in Figure 2. To investigate potential sources of heterogeneity, subgroup analyses were subsequently performed according to region, age group, definitions, and measurement tools.

Figure 2.

Figure 2

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Forest plot for the prevalence of healthy aging among included studies

Subgroup Analysis

Subgroup analyses were performed based on age, gender, marital status, educational attainment, economic status, community participation, living situation, economic region, occupational status, place of residence, smoking, alcohol consumption, physical activity, BMI, hearing impairment, visual impairment, hypertension, and diabetes. It is noted that the analyses were not mutually exclusive; the same study may be included in multiple subgroups due to overlapping characteristics of the study populations. Substantial heterogeneity persisted across the subgroups (I² > 50%, p < 0.1), and random-effects models were utilized for all analyses.

Results indicated that the prevalence of healthy aging for the age group 60-74 years was 0.16 (95% CI: 0.12–0.21), while for those aged 75 years and older, it was 0.06 (95% CI: 0.03–0.10). Gender analysis showed that the prevalence for males was 0.23 (95% CI: 0.18–0.28) and for females, it was 0.18 (95% CI: 0.14–0.22). For marital status, the prevalence among married individuals was 0.23 (95% CI: 0.12–0.37), compared to 0.15 (95% CI: 0.11–0.21) for unmarried, divorced, widowed, or separated individuals. Regarding educational attainment, those with senior high school and above had a prevalence of 0.25 (95% CI: 0.20–0.31), while those with junior high school and below had a prevalence of 0.15 (95% CI: 0.11–0.20). Additionally, individuals with a poor economic level had a prevalence of 0.15 (95% CI: 0.10–0.21), whereas those with a good economic level had a prevalence of 0.23 (95% CI: 0.18–0.29). Community participation yielded a prevalence of 0.23 (95% CI: 0.08–0.43) for participants versus 0.12 (95% CI: 0.03–0.27) for non-participants. Living situation analysis revealed that those living alone had a prevalence of 0.22 (95% CI: 0.03–0.50), while those not living alone had a prevalence of 0.23 (95% CI: 0.05–0.49).

Economic classification indicated a prevalence of 0.24 (95% CI: 0.12–0.39) in high-income countries compared to 0.21 (95% CI: 0.14–0.28) in low- and middle-income countries. Analysis by employment status indicated that individuals classified as “Working” had a prevalence of 0.32 (95% CI: 0.20–0.47), whereas those classified as “Not Working” had a prevalence of 0.15 (95% CI: 0.12–0.19). Rural participants exhibited a prevalence of 0.22 (95% CI: 0.13–0.33), while urban participants had a prevalence of 0.31 (95% CI: 0.23–0.39).

Regarding smoking status, the prevalence among smokers was 0.19 (95% CI: 0.13–0.26), while for non-smokers, it was 0.23 (95% CI: 0.17–0.30). Alcohol consumption yielded a prevalence of 0.24 (95% CI: 0.15–0.35) for drinkers and 0.19 (95% CI: 0.13–0.26) for non-drinkers. Physical activity analysis showed a prevalence of 0.25 (95% CI: 0.17–0.34) for those who exercised and 0.17 (95% CI: 0.11–0.23) for those who did not.

BMI categorization indicated that individuals within the normal range had a prevalence of 0.28 (95% CI: 0.20–0.36), whereas those outside the normal range had a prevalence of 0.27 (95% CI: 0.20–0.35). Hearing impairment analysis showed a prevalence of 0.09 (95% CI: 0.04–0.17) for those with hearing impairment and 0.12 (95% CI: 0.07–0.20) for those without. Similarly, visual impairment results showed a prevalence of 0.09 (95% CI: 0.05–0.14) for those with visual impairment and 0.11 (95% CI: 0.07–0.17) for those without. Hypertension prevalence was 0.17 (95% CI: 0.06–0.33) for those with hypertension and 0.20 (95% CI: 0.09–0.35) for those without. Finally, diabetes prevalence was 0.21 (95% CI: 0.04–0.45) for those with diabetes and 0.22 (95% CI: 0.11–0.37) for those without. Detailed results of the subgroup analysis for global healthy aging can be found in Table 2.

Table 2.

Subgroup analysis of the prevalence of healthy aging

Subgroup Classification Number of Included Studies Sample Size Heterogeneity Test Effect Model Prevalence 95% CI
p-value I2 (%)
Age 60~74 11 87530 <0.001 99.22 Random 0.16 (0.12-0.21)
75~ 12 90211 <0.001 99.16 Random 0.06 (0.03-0.10)
Gender Male 25 102150 <0.001 99.71 Random 0.23 (0.18-0.28)
Female 25 125849 <0.001 99.66 Random 0.18 (0.14-0.22)
Marital Status Married 19 125129 <0.001 99.96 Random 0.23 (0.12-0.37)
Single/Widowed/Divorced/Separated, etc. 19 73948 <0.001 99.69 Random 0.15 (0.11-0.21)
Education Level Junior high school and below 18 117324 <0.001 99.71 Random 0.15 (0.11-0.20)
High school and above 18 72513 <0.001 99.55 Random 0.25 (0.20-0.31)
Economic Level Poor 14 60309 <0.001 99.56 Random 0.15 (0.10-0.21)
Good 14 52105 <0.001 99.41 Random 0.23 (0.18-0.29)
Social Engagement Participate 3 6041 <0.001 99.26 Random 0.23 (0.08-0.43)
Non-participation 3 2726 <0.001 97.56 Random 0.12 (0.03-0.27)
Living Conditions Living alone 4 3005 <0.001 99.60 Random 0.22 (0.03-0.50)
Not living alone 4 12760 <0.001 99.88 Random 0.23 (0.05-0.49)
Economy High-income countries 19 171524 <0.001 99.97 Random 0.24 (0.12-0.39)
Low-middle-income countries 20 83803 <0.001 99.84 Random 0.21 (0.14-0.28)
Employment Status Working 4 46784 <0.001 99.86 Random 0.32 (0.20-0.47)
Not Working 4 86655 <0.001 99.35 Random 0.15 (0.12-0.19)
Residence Rural 6 69900 <0.001 99.85 Random 0.22 (0.13-0.33)
Urban 6 49271 <0.001 99.62 Random 0.31 (0.23-0.39)
Smoking Status Yes (including former smokers) 17 56026 <0.001 99.68 Random 0.19 (0.13-0.26)
No 17 99507 <0.001 99.78 Random 0.23 (0.17-0.30)
Alcohol Consumption Drinkers 13 14913 <0.001 99.49 Random 0.24 (0.15-0.35)
Non-drinkers 13 131710 <0.001 99.85 Random 0.19 (0.13-0.26)
Physical Activity Yes 15 47810 <0.001 99.79 Random 0.25 (0.17-0.34)
No 15 98570 <0.001 99.71 Random 0.17 (0.11-0.23)
BMI Level Within normal range 8 65820 <0.001 99.71 Random 0.28 (0.20-0.36)
Outside normal range 8 54651 <0.001 99.70 Random 0.27 (0.20-0.35)
Hearing Impairment Yes 4 1703 <0.001 95.09 Random 0.09 (0.04-0.17)
No 4 1416 <0.001 98.50 Random 0.12 (0.07-0.20)
Visual Impairment Yes 5 3802 <0.001 95.11 Random 0.09 (0.05-0.14)
No 5 12093 <0.001 97.79 Random 0.11 (0.07-0.17)
Hypertension Yes 4 6824 <0.001 99.27 Random 0.17 (0.06-0.33)
No 4 7315 <0.001 99.24 Random 0.20 (0.09-0.35)
Diabetes Yes 3 4280 <0.001 99.56 Random 0.21 (0.04-0.45)
No 3 7989 <0.001 98.70 Random 0.22 (0.11-0.37)
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Note. Subgroup analyses were not mutually exclusive; the same study may be included in more than one subgroup

Meta-analysis Results of Associated Factors on Healthy Aging

A meta-analysis was performed on 13 influencing factors. The heterogeneity test results for the factors of marriage, alcohol consumption, and self-rated health indicated I² < 50% and p > 0.10; the analysis indicated low heterogeneity across study results, supporting the use of a fixed-effects model. However, heterogeneity tests for factors such as age, gender, educational level, economic status, social participation, employment status, living area, smoking, physical activity, and BMI revealed I² > 50% and p < 0.10, indicating substantial heterogeneity; Consequently, a random-effects model was adopted.

The meta-analysis findings indicated that older age (≥75 years), female gender, smoking, alcohol consumption, and BMI outside the normal range were identified as risk factors for healthy aging (p < 0.05). In contrast, protective factors for healthy aging included being married, higher educational attainment, good economic level, high levels of social participation, being employed, residing in urban areas, engaging in physical activity, and possessing good self-rated health (p < 0.05). The detail is provided in Table 3.

Table 3.

Subgroup analysis of factors related to healthy aging

Related Factors Reference group Subgroups Number of Included Studies Sample Size Heterogeneity Test Effect Model OR 95% CI p-value
I2(%) p-value
Age (Year) 60-74 75 and over 24 174632 98.6% <0.001 Random 0.649 (0.579-0.720) <0.001
Gender Male Female 11 142023 62.7% <0.001 Random 0.630 (0.570-0.691) <0.001
Marital Status Unmarried/Divorced Married 6 46803 0.0% 0.655 Fixed 1.341 (1.223-1.458) <0.001
Educational Level Junior high school and below High school and above 13 140130 93.7% <0.001 Random 1.703 (1.426-1.981) <0.001
Economic Level Poor Good 12 131195 75.1% <0.001 Random 1.552 (1.349-1.754) <0.001
Social Participation Non-Participation Participation 6 91442 68.6% 0.007 Random 1.146 (1.066-1.226) <0.001
Employment Status Non-Employment Employed 3 127558 99.8% <0.001 Random 2.435 (1.148-3.722) <0.001
Residential Rural Urban 3 100952 80.4% 0.006 Random 1.475 (1.177-1.772) <0.001
Smoking Status Non-smokers Smokers 6 28687 75.1% 0.001 Random 0.706 (0.627-0.786) <0.001
Alcohol Consumption Non-drinkers drinkers 3 25533 0.0% 0.670 Fixed 0.838 (0.798-0.879) <0.001
Physical Activity Non-Exercise Exercise 12 145873 89.30% <0.001 Random 1.565 (1.398-1.733) <0.001
BMI Levels Within normal range Outside normal range 7 118306 89.5% <0.001 Random 0.762 (0.655- 0.869) <0.001
Self-Rated Health Status Poor Good 5 17127 38.7% 0.163 Fixed 2.239 (1.779-2.700) <0.001
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Sensitivity Analysis

Sensitivity analysis conducted on the 39 included studies indicated that no single study significantly influenced the overall meta-analysis results, suggesting that the findings of this study are robust. Detailed results of the sensitivity analysis are presented in the accompanying Figure 3.

Figure 3.

Figure 3

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Leave-one-out sensitivity analysis of the prevalence of healthy aging

Publication Bias

Publication bias was assessed using funnel plot analysis, Egger’s linear regression test, Begg’s rank correlation test, and the trim-and-fill method, all implemented in R version 4.5.0 with the “meta” package (version 8.1-0). The original funnel plot (Figure 4) suggested slight asymmetry, indicating possible publication bias. Egger’s test did not show statistically significant funnel plot asymmetry (t = 1.46, df = 37, p = 0.154). Similarly, Begg’s test indicated no significant publication bias (z = 1.78, p = 0.075).

Figure 4.

Figure 4

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Original funnel plot for publication bias in the meta-analysis of healthy aging prevalence

Trim-and-fill analysis imputed 10 potentially missing studies to achieve funnel plot symmetry. The adjusted funnel plot is shown in Figure 5. The unadjusted pooled prevalence of healthy aging was 23.0% (95% CI: 18%–27%). After adjustment for publication bias using the trim-and-fill method, the pooled prevalence decreased to 15.6% (95% CI: 11.0%–20.1%) using a random-effects model. The adjustment slightly reduced the pooled estimate, suggesting that publication bias may have contributed to a slight overestimation in the original analysis, but the overall findings remained robust. Heterogeneity among studies remained very high (I² = 99.9%), indicating substantial variability in effect sizes across included studies.

Figure 5.

Figure 5

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Funnel plot with trim-and-fill adjustment in the meta-analysis of healthy aging prevalence

Discussion

Principal Findings

Healthy aging, a global strategy endorsed by the United Nations and WHO, holds profound significance. Considering the growing trend of global aging, implementing healthy aging strategies can mitigate health inequalities and alleviate the pressure that the increasing elderly population places on healthcare systems and socioeconomic development.

Despite the initiation of healthy aging strategies in the 1990s, which have evolved through concepts like successful aging and active aging, the WHO redefined healthy aging in 2015 and launched the Healthy Aging Action Plan for 2020-2030 (WHO, 2020). Our meta-analysis of 39 studies revealed a healthy aging prevalence of only 23%, indicating that the overall health and living conditions of older people require significant attention. After adjusting for potential publication bias using the trim-and-fill method, the estimate decreased to 15.6% (95% CI: 11.0%–20.1%)

This reduction likely reflects the inclusion of potentially missing studies with smaller sample sizes and lower prevalence estimates, thereby balancing funnel plot asymmetry. In healthy aging research, studies reporting higher prevalence are more likely to be published, while those with lower prevalence are rarely disseminated, a trend consistent with broader evidence on selective publication in health research (Fanelli et al., 2017; Song et al., 2010). Consequently, the unadjusted prevalence may partly reflect publication bias, whereas the adjusted figure provides a more conservative and potentially more accurate estimate. Even after this adjustment, the prevalence remains low, underscoring a substantial gap in achieving global healthy aging targets, especially in regions with limited resources and pronounced health disparities.

The prevalence of healthy aging is associated with various factors. The analysis shows that as age increases, the prevalence of healthy aging significantly declines. This is likely due to the cumulative physiological changes associated with aging, leading to the onset of frailty and the increased incidence of chronic diseases such as diabetes and hypertension, which accelerate functional decline (Kim & Jazwinski, 2015). This underscores the need for healthcare professionals to identify controllable risk factors for chronic diseases and implement scientific management strategies to reduce their incidence (Namkung & Kang, 2024). Additionally, effective health management for older adults, especially those of advanced age, is critical in maintaining their functional capacity.

Gender differences significantly impact healthy aging, with men exhibiting a higher prevalence than women. Women are biologically predisposed to aging more rapidly, leading to a higher likelihood of frailty (Phyo et al., 2024). Furthermore, women generally have longer life expectancies, which increases their exposure to disabilities and disease burdens. Therefore, it is essential to recognize these gender differences in elderly health management, particularly by addressing the health needs of older women and implementing timely health interventions tailored to their physiological characteristics. Regular health screenings are vital for older women, particularly for preventing and managing osteoporosis.

Marital status also plays a crucial role in health outcomes for older adults. Married individuals typically benefit from better family support, which reduces feelings of loneliness and correlates with improved health outcomes (Wang & Yi, 2023). Moreover, higher educational levels are associated with a greater prevalence of healthy aging. This correlation may stem from the relationship between education and social status, as those with higher education often enjoy better job opportunities and income during their youth. Individuals with higher educational attainment are also more likely to adopt healthier lifestyles and prioritize mental well-being, leading to enhanced health in old age (Wu et al., 2020).

Economic status further influences healthy aging prevalence; older adults with better financial conditions report higher healthy aging rates. This may be due to their greater investment in health, better living conditions, and access to nutritious food and healthcare services. Social participation also significantly impacts healthy aging; those who engage actively in social activities tend to experience higher rates of healthy aging. Social engagement fosters positive physical and mental health outcomes by reducing loneliness and enhancing self-efficacy (Lin et al., 2024).

Conversely, older adults living alone often exhibit lower healthy aging rates. This may be related to the lack of social support for the majority of older persons living alone, who may lack the care of family members, live in relatively poor conditions, and lack the necessary support in the management of chronic diseases, the combination of which affects the health status of older persons (Ng et al., 2015). This vulnerability necessitates increased attention from family members towards the well-being of solitary seniors. Community workers should also play a vital role in providing convenient health services and organizing community activities for these individuals, fostering a supportive community environment.

Working older adults demonstrate a higher prevalence of healthy aging compared to their retired counterparts. Research suggests that those who cease working may face declines in mental health and functional abilities, leading to social isolation and increased mortality risks (Minami et al., 2015). The productive aging theory posits that older adults represent valuable human resources and can contribute meaningfully to their families, communities, and society. Hence, for those physically able, continued work may enhance their sense of purpose, alleviate loneliness, and slow the aging process.

Significant urban-rural disparities exist in healthy aging prevalence, with urban older adults faring better than their rural counterparts. In many developing countries, including China, substantial disparities in development levels persist between urban and rural areas. As younger populations migrate to economically prosperous cities, rural elderly face challenges such as isolation, lack of economic resources, and inadequate healthcare access (Ying et al., 2020).

The analysis further indicates that smoking negatively impacts healthy aging, with smokers showing significantly lower prevalence rates than non-smokers. Smoking increases the risk of numerous diseases, particularly lung cancer and chronic obstructive pulmonary disease (COPD), along with being a major risk factor for stroke and osteoporosis (West, 2017). With over 1.1 billion smokers worldwide in 2017, tobacco use poses significant threats to elderly health, emphasizing the need for strengthened tobacco control strategies (Flor et al., 2021).

Interestingly, our study found that alcohol consumption (classified simply as drinkers versus non-drinkers) is associated with higher rates of healthy aging, consistent with findings from Daskalopoulou et al. (2018). While this could suggest that alcohol consumption, possibly in moderate amounts, may have beneficial effects on cardiovascular health and healthy aging (Probst et al., 2020), the limitations in our study—particularly the lack of detailed categorization of alcohol intake—mean we cannot directly attribute these effects to moderate drinking. Further large-scale longitudinal studies with precise definitions of drinking patterns are needed to confirm these associations. It is essential to emphasize that, given the potential health risks associated with alcohol use, these findings should not be interpreted as a recommendation for alcohol consumption as a health intervention. Physical exercise is positively correlated with healthy aging; those who engage in regular exercise demonstrate higher prevalence rates. Exercise has been shown to delay the aging process, reduce all-cause mortality (Feng et al., 2023), and lower the risk of cardiovascular disease and cancer-related deaths (Iso-Markku et al., 2024). Therefore, older adults should incorporate appropriate physical activity into their routines, and communities should facilitate environments conducive to such activities.

Abnormal BMI negatively affects older adults’ health, with high BMI increasing the incidence of hypertension and diabetes (Soltani et al., 2021), while low BMI raises the risk of malnutrition and functional decline (Kuzuya, 2021). Maintaining a normal weight is crucial for reducing disease risks. Additionally, sensory impairments such as hearing and vision loss are linked to lower healthy aging rates. Hearing loss can hinder social interactions (Livingston et al., 2020), heightening feelings of loneliness and cognitive decline, while vision impairments can affect daily functioning and increase fall risks, leading to depression and cognitive deficits (Nagarajan et al., 2022). Regular screening for hearing and vision is essential for older adults to sustain optimal sensory functions.

Chronic conditions like hypertension and diabetes elevate the risk of cardiovascular diseases and other health issues among older adults, with over 1.4 billion individuals suffering from hypertension and more than 400 million from diabetes globally (Petrie et al., 2018; Zheng et al., 2018). Future efforts should prioritize effective management strategies for these chronic conditions.

Our meta-analysis results indicate that advanced age, female gender, smoking, excessive alcohol consumption, and abnormal BMI negatively impact healthy aging. Conversely, marriage, financial stability, high levels of social participation, continued employment, urban residency, regular physical activity, and positive self-rated health emerge as protective factors. These findings reveal the multifaceted nature of elderly health management, suggesting that healthy aging is a complex systemic endeavor encompassing individual, familial, societal, and policy dimensions.

Future research should focus on controllable factors influencing healthy aging across diverse social contexts, particularly examining the differences in lifestyles and health behaviors of older adults in varying cultural and economic environments. Understanding their unique challenges and needs is crucial. Interdisciplinary collaboration among fields such as clinical medicine, geriatrics, gerontology, nursing, public health, and engineering will be vital in creating supportive environments for aging populations. Through comprehensive interventions and support systems, we aim to foster improved health outcomes and enhance the quality of life for older adults.

Strengths and Limitations

This study employed a comprehensive and up-to-date literature search, included a large and diverse sample from multiple regions, and applied rigorous quality assessment to all included studies. Extensive subgroup analyses were conducted to provide deeper insights into related factors and potential sources of heterogeneity. However, the pooled prevalence estimates exhibited extremely high heterogeneity (I² = 99.9%), largely attributable to differences in study populations, definitions, assessment instruments, and measurement thresholds for healthy aging. In particular, the association between employment status and healthy aging showed extremely high heterogeneity and wide confidence intervals, indicating that this result should be interpreted with great caution. Other associations also varied substantially across studies. Although the number of included studies was sufficient for some variables, meta-regression was not performed due to the substantial variability in definitions and the inconsistent reporting of key covariates across studies. Furthermore, the distribution of certain covariates was highly unbalanced, and important data were missing for several variables in many studies. Under these circumstances, meta-regression would likely produce unreliable or uninterpretable results; therefore, subgroup analyses were used instead to explore potential sources of heterogeneity.

Consequently, the findings should be interpreted as providing a broad overview rather than precise estimates for specific contexts. The lack of standardized definitions and measurement tools across studies remains a common challenge in this research field and may limit the validity, comparability, and generalizability of the results. Future research should adopt unified definitions, standardized measurements, and multicenter designs to strengthen the evidence base.

Conclusion

The findings from this systematic review and meta-analysis reveal a global healthy aging prevalence of 23% and identify multiple influencing factors. The analysis demonstrates that advancing age (especially for those 75 years and older), female gender, smoking, excessive alcohol consumption, and abnormal BMI significantly increase the risk of unhealthy aging. Conversely, being married, attaining higher educational levels, possessing good financial conditions, engaging in social participation, remaining employed, residing in urban environments, maintaining regular physical activity, and having positive self-assessments of health serve as protective factors against unhealthy aging. These insights highlight the complexity of elderly health management, indicating that achieving healthy aging is a multidimensional endeavor involving personal, familial, societal, and policy aspects. Future research should prioritize the influence of controllable factors on healthy aging within diverse social contexts, particularly exploring differences in lifestyles and health behaviors of older adults across varying cultural and economic environments to better understand their unique challenges and needs. Moreover, interdisciplinary collaboration among fields such as clinical medicine, geriatrics, gerontology, nursing, public health, and engineering is essential to create optimal living conditions for older people. Through multifaceted interventions and support systems, we aim to enhance the overall health and well-being of older adults.

Supplementary Material

BNJ-11-5-504-s1.pdf (411.1KB, pdf)

Acknowledgment

Linxi Tang would like to express his heartfelt thanks to Dr. Azlina Yusuf and Dr. Nur Syahmina Binti Rasudin for their kind supervision, academic advice, and continuous encouragement throughout this research process.

Funding Statement

Funding This research received no external funding.

Declaration of Conflicting Interest

The authors declare no conflict of interest to declare.

Authors’ Contributions

Supervision, Azlina Yusuf, Nur Syahmina Binti Rasudin; Conceptualization, Tang Linxi, Azlina Yusuf and Nur Syahmina Binti Rasudin; Data curation, Tang Linxi, Dong Yuan; Formal analysis, Nur Syahmina Binti Rasudin, Tang Linxi, and Azlina Yusuf; Methodology, Azlina Yusuf, Nur Syahmina Binti Rasudin and Tang Linxi; Software, Tang linxi, Azlina Yusuf, Nur Syahmina Binti Rasudin and Dong Yuan; Writing – original draft: Tang Linxi; Writing – review & editing, Azlina Yusuf, Nur Syahmina Binti Rasudin and Dong Yuan. All authors have read and agreed to the published version of the manuscript.

Authors’ Biographies

Linxi Tang is a PhD Candidate at the School of Health Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Malaysia.

Dr. Nur Syahmina Binti Rasudin is a Lecturer at the School of Health Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Malaysia.

Yuan Dong is a PhD Candidate at the School of Medical Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Malaysia.

Dr. Azlina Yusuf is a Lecturer at the School of Health Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Malaysia.

Ethical Consideration

Ethical approval was not required for this study as it is a systematic review and meta-analysis based on previously published literature.

Data Availability

The datasets and/or analyzed during the current study are available from the corresponding author upon reasonable request. The Supplementary File provide detailed supporting information, including the PRISMA 2020 checklist (S1) and abstract guidelines (S2), the full search strategy (S3), quality assessment tools with corresponding evaluation results (S4), assessment outcomes of cross-sectional (S5) and longitudinal studies (S7), and the Newcastle–Ottawa Quality Assessment Scale (S6).

Declaration of Use of AI in Scientific Writing

The authors used ChatGPT in the writing process to improve readability and language clarity. However, all content, including data interpretation and final conclusions, was written and reviewed by the authors, who take full responsibility for the work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

BNJ-11-5-504-s1.pdf (411.1KB, pdf)

Data Availability Statement

The datasets and/or analyzed during the current study are available from the corresponding author upon reasonable request. The Supplementary File provide detailed supporting information, including the PRISMA 2020 checklist (S1) and abstract guidelines (S2), the full search strategy (S3), quality assessment tools with corresponding evaluation results (S4), assessment outcomes of cross-sectional (S5) and longitudinal studies (S7), and the Newcastle–Ottawa Quality Assessment Scale (S6).

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